Felix Grün

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 97 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 53 citations into it
• Artificial Intelligence — 31 citations into it
• Biophysics — 8 citations into it
• Safety, Robustness & Interpretability — 7 citations into it

Whom Felix Grün trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Matthew D. Zeiler
2. Alexey Dosovitskiy
3. Geoffrey E. Hinton
4. Andrea Vedaldi
5. Alex Krizhevsky
6. Rob Fergus
7. Yoshua Bengio
8. Kaiming He
9. Yann LeCun
10. Diederik P. Kingma

Who trusts Felix Grün overall

1. Gabrielle Ras
2. Bilal Alsallakh
3. Christin Seifert
4. Marcel A. J. van Gerven
5. Amin Jourabloo

Papers from Felix Grün's perspective

Ordered by how strongly the researchers Felix Grün trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• The Liver Tumor Segmentation Benchmark (LiTS) (2019) — 1,500 citations
• Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks (2017) — 365 citations
• A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks (2016) — 77 citations